Data Science on Big Data: data preprocessing, learning models, descriptive models and data visualization

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Guest Editors
Prof. Sebastián Ventura
University of Cordoba, Spain

Prof. Jose Maria Luna Ariza
University of Cordoba, Spain

Manuscript Topics
We are living in the Big Data era so data is being gathered from multiple sources without noticing. Taking advantage of such large amount of data is, nowadays, an important issue for companies, government or even small groups of people. Data Science is therefore being transformed to handle and manage such data. In this regard, traditional tasks for Data Science (data preprocessing, learning models, descriptive models, data visualization, etc) are being considered from the Big Data perspective and, more specifically, from a distributed point of view thanks to trending frameworks such as MapReduce. Despite the fact that research community on these topics is growing and the number of related works is extensive, it is necessary to continue with the design and development of more efficient models in any of the aforementioned areas. Some of the aforementioned tasks, such as data visualization, are essential in many application fields in which Big Data should provide the right insight and in a proper way to be understood by expert in the fields which do not have knowledge on data science. The aim of this special issue therefore to significantly enhance the state-of-the-art in Data Science in general, and Big Data in particular. We exhort authors across the world to submit their original and unpublished works. We have an especial interest in works focusing on the topics listed below but works attending other approaches will also be well received.

• Big Data
• Big Data applications
• Data preprocessing
• Machine Learning
• Predictive models
• Descriptive analytics
• Label space reduction
• Deep Learning
• Data visualization

Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is July 30, 2020

Instructions for authors
Please submit your manuscript to online submission system

Izzat Alsmadi, Michael J. OBrien
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